HoneyHive

HoneyHive is an AI observability and evaluation platform that integrates development, testing, and monitoring for LLM agents.

The platform supports evaluation through custom code, LLM, and human evaluators applied to prompts, agents, and pipelines. Users define test suites and run them pre-deployment to detect failures. CI automation integrates with GitHub Actions via the SDK, enabling regression checks on commits. Distributed tracing provides visibility into pipeline steps. Reports version and compare runs, while dataset management captures production data for curation. Pre-built evaluators cover metrics like context relevance and toxicity. Custom evaluators handle specific needs, such as JSON validation or moderation. Infrastructure parallelizes runs for efficiency on large suites.

Observability features include end-to-end tracing with OpenTelemetry for chains, agents, and RAG pipelines. The SDK logs data synchronously or asynchronously in Python and TypeScript. Logs enrich with metadata and user feedback. Monitoring computes metrics via online evaluators, detecting failures in faithfulness or sentiment. Custom charts and filters enable RAG and agent analytics. Human review annotates traces for fine-tuning. Alerts notify on drift or anomalies. Auto-instrumentation works with providers like OpenAI and tools like Pinecone.

Artifact management centralizes prompts, tools, datasets, and evaluators, syncing UI and code changes. The Playground supports live collaboration on prompt templates and functions, with version control and one-click deployments. It accesses over 100 models via integrations with GPU clouds and databases like SerpAPI. Enterprise options include SOC-2 Type II, GDPR, and HIPAA compliance, with hosting choices: multi-tenant SaaS, single-tenant, or self-hosted. RBAC handles permissions across projects.

The offering includes a free Developer tier with 10,000 events monthly, unlimited workspaces, and 30-day retention. Enterprise provides custom events, unlimited metrics, and advanced security like SAML SSO. Billing bases on events, defined as trace spans or metric combinations via OTLP or JSON. Compared to LangSmith, HoneyHives open standards reduce lock-in. Versus Arize Phoenix, it emphasizes agent tracing over general ML metrics.

Users appreciate intuitive dashboards and collaboration for faster iterations. Some note the free tiers event limit suits small projects but scales via custom plans. A surprise comes from AI-assisted root cause analysis in traces, accelerating debugging.

Integrate the SDK into your next agent prototype and run an initial eval suite to baseline performance.